# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import sys
import unittest

import numpy as np
from transformers import AutoTokenizer

import mindspore as ms
from mindspore import mint

from mindone.diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, Lumina2Pipeline, Lumina2Transformer2DModel
from mindone.diffusers.utils.testing_utils import floats_tensor
from mindone.transformers import GemmaForCausalLM

sys.path.append(".")

from utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set  # noqa: E402


class Lumina2LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
    pipeline_class = Lumina2Pipeline
    scheduler_cls = FlowMatchEulerDiscreteScheduler
    scheduler_classes = [FlowMatchEulerDiscreteScheduler]
    scheduler_kwargs = {}

    transformer_kwargs = {
        "sample_size": 4,
        "patch_size": 2,
        "in_channels": 4,
        "hidden_size": 8,
        "num_layers": 2,
        "num_attention_heads": 1,
        "num_kv_heads": 1,
        "multiple_of": 16,
        "ffn_dim_multiplier": None,
        "norm_eps": 1e-5,
        "scaling_factor": 1.0,
        "axes_dim_rope": [4, 2, 2],
        "cap_feat_dim": 8,
    }
    transformer_cls = Lumina2Transformer2DModel
    vae_kwargs = {
        "sample_size": 32,
        "in_channels": 3,
        "out_channels": 3,
        "block_out_channels": (4,),
        "layers_per_block": 1,
        "latent_channels": 4,
        "norm_num_groups": 1,
        "use_quant_conv": False,
        "use_post_quant_conv": False,
        "shift_factor": 0.0609,
        "scaling_factor": 1.5035,
    }
    vae_cls = AutoencoderKL
    tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/dummy-gemma"
    text_encoder_cls, text_encoder_id = GemmaForCausalLM, "hf-internal-testing/dummy-gemma-diffusers"

    @property
    def output_shape(self):
        return (1, 4, 4, 3)

    def get_dummy_inputs(self, with_generator=True):
        batch_size = 1
        sequence_length = 16
        num_channels = 4
        sizes = (32, 32)

        generator = np.random.default_rng(0)
        noise = floats_tensor((batch_size, num_channels) + sizes)
        input_ids = mint.randint(1, sequence_length, (batch_size, sequence_length), generator=ms.manual_seed(0))

        pipeline_inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "height": 32,
            "width": 32,
            "output_type": "np",
        }
        if with_generator:
            pipeline_inputs.update({"generator": generator})

        return noise, input_ids, pipeline_inputs

    @unittest.skip("Not supported in Lumina2.")
    def test_simple_inference_with_text_denoiser_block_scale(self):
        pass

    @unittest.skip("Not supported in Lumina2.")
    def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
        pass

    @unittest.skip("Not supported in Lumina2.")
    def test_modify_padding_mode(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Lumina2.")
    def test_simple_inference_with_partial_text_lora(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Lumina2.")
    def test_simple_inference_with_text_lora(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Lumina2.")
    def test_simple_inference_with_text_lora_and_scale(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Lumina2.")
    def test_simple_inference_with_text_lora_fused(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Lumina2.")
    def test_simple_inference_with_text_lora_save_load(self):
        pass

    def test_lora_fuse_nan(self):
        for scheduler_cls in self.scheduler_classes:
            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
                pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
                )

            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
            denoiser.add_adapter(denoiser_lora_config, "adapter-1")
            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")

            # corrupt one LoRA weight with `inf` values
            pipe.transformer.layers[0].attn.to_q.lora_A["adapter-1"].weight += float("inf")

            # with `safe_fusing=True` we should see an Error
            with self.assertRaises(ValueError):
                pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)

            # without we should not see an error, but every image will be black
            pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
            out = pipe(**inputs)[0]

            self.assertTrue(np.isnan(out).all())
